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Cisco 300-640 Exam Syllabus Topics:

SectionWeightObjectives
AI Infrastructure Fundamentals15%- GPU Architecture and Types
- AI Ecosystem Components
- AI Infrastructure Requirements
- AI Workload Characteristics
AI Infrastructure Operations20%- Troubleshooting Common Issues
- Performance Optimization
- Capacity Planning
- Monitoring and Management Tools
AI Infrastructure Security and Compliance10%- Access Control and Authentication
- Compliance Considerations
- Security Best Practices
Cisco AI Infrastructure Architecture30%- Network Architecture for AI Workloads
- Cisco UCS AI Infrastructure Solutions
- Storage Architecture Considerations
- Hyperconverged Infrastructure for AI
AI Infrastructure Deployment25%- Storage Configuration for AI
- GPU Server Configuration
- Hardware Installation and Configuration
- Network Switch Configuration
- Cabling and Connectivity

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Cisco Implementing Cisco Data Center AI Infrastructure Sample Questions (Q21-Q26):

NEW QUESTION # 21
A data center is deploying an AI infrastructure for LLM training and model inference. This infrastructure requires high GPU density, low-latency interconnects, and the ability to scale compute resources. When considering the Cisco UCS C885A M8 Rack Server for the deployment, which design aspect is the most critical for achieving optimal performance and scalability in this environment?

Answer: B

Explanation:
For UCS C885A M8 servers running LLM training and inference workloads, the surrounding Nexus fabric design is critical because GPU-dense systems depend on low-latency, high- bandwidth, lossless network connectivity for scale-out performance. Selecting and configuring the correct Nexus fabric minimizes communication delays between compute nodes and enables the AI infrastructure to scale efficiently.


NEW QUESTION # 22
A network engineer is deploying an AI training fabric using Cisco Nexus switches with RoCEv2 enabled. During testing, packet drops occur when traffic bursts exceed buffer capacity. Which feature should be configured to minimize congestion and improve lossless Ethernet performance for GPU traffic?

Answer: A

Explanation:
Priority Flow Control (PFC) allows selective pausing of traffic classes during congestion, which is critical for RoCEv2 environments carrying GPU-to-GPU communication. AI workloads are highly sensitive to packet loss and retransmissions. BPDU Guard and DHCP Snooping are security features, while Storm Control only limits broadcast or multicast traffic and does not create lossless behavior for RDMA traffic.


NEW QUESTION # 23
What does workload distribution offer in an AI infrastructure with local and external resources?

Answer: D

Explanation:
Workload distribution in AI infrastructure enables computational tasks to be spread across local and external resources, improving scalability, resource utilization, and execution flexibility across multiple processing locations.


NEW QUESTION # 24
A global enterprise is deploying a new AI-driven analytics platform that requires high-performance GPU acceleration, large memory capacity, and robust virtualization support. The compute and GPU resources selected must provide direct GPU to GPU access leveraging NVLink technology.
Which set of products meets the requirements?

Answer: D

Explanation:
Cisco UCS C885A M8 with NVIDIA H200 GPUs is designed for high-performance AI workloads that require dense GPU acceleration, large memory capacity, and direct GPU-to-GPU communication through NVIDIA NVLink. This combination supports demanding analytics, training, and inference workloads where high-bandwidth GPU interconnect is required.


NEW QUESTION # 25
A network operations team deploys HyperFabric AI and wants to monitor fabric health to proactively identify performance issues. The team must track metrics specifically relevant to AI training workloads. Which metric is most critical for identifying congestion issues that could impact AI training performance in a HyperFabric AI deployment?

Answer: A

Explanation:
PFC pause frame transmission rate is the key congestion indicator for lossless Ethernet fabrics used by AI training workloads. A high or increasing pause frame rate shows that interfaces are experiencing congestion and applying flow control, which can directly affect GPU-to-GPU communication performance in a HyperFabric AI environment.


NEW QUESTION # 26
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